Cel Scholars studying contemporary conflict and development have increasingly turned to the historical roots of state formation. Going beyond Tilly’s classical theory of state formation, the NASTAC project creates a new theory of nationalist state transformation that will be evaluated with historical maps and archival data extracted through machine learning. While much has been written about state formation and nationalism, there is currently no empirically verified theory that shows how nationalism transformed and keeps transforming state internal and external properties, and how post-nationalist mechanisms counteract this influence. Without such a framework it is difficult to know under what conditions partition or power sharing should be used to pacify conflict-ridden multi-ethnic states. The project contains four work packages (WPs). WP1 will investigate whether state penetration and the evolution of the size and shapes of states have interacted with warfare according to Tilly’s expectations. WP2 will develop our theory of nationalist state transformation showing how nationalism affected internal state reach and how it triggered external change, such as secession, unification and irredentism, and, in turn, how these processes interacted with, and modified, patterns of conflict. WP3 will apply the theory of nationalist state transformation to the post-1945 world and will analyze how it interacts with post-nationalist mechanisms, such as power sharing. WP4 will develop innovative methods that draw on recent advances in machine learning techniques, such as deep learning, in order to transform historical maps and documents into disaggregated and spatially explicit datasets with extensive historical scope. Led by Lars-Erik Cederman, the NASTAC project will be hosted by the International Conflict Research group at ETH Zürich, which has published extensively in top outlets and has ample experience in managing large research projects. Dziedzina nauki natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning Program(-y) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Temat(-y) ERC-2017-ADG - ERC Advanced Grant Zaproszenie do składania wniosków ERC-2017-ADG Zobacz inne projekty w ramach tego zaproszenia System finansowania ERC-ADG - Advanced Grant Instytucja przyjmująca EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH Wkład UE netto € 2 631 556,00 Adres Raemistrasse 101 8092 Zuerich Szwajcaria Zobacz na mapie Region Schweiz/Suisse/Svizzera Zürich Zürich Rodzaj działalności Higher or Secondary Education Establishments Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Uczestnictwo w unijnych programach w zakresie badań i innowacji Opens in new window sieć współpracy HORIZON Opens in new window Koszt całkowity € 2 631 556,00 Beneficjenci (1) Sortuj alfabetycznie Sortuj według wkładu UE netto Rozwiń wszystko Zwiń wszystko EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH Szwajcaria Wkład UE netto € 2 631 556,00 Adres Raemistrasse 101 8092 Zuerich Zobacz na mapie Region Schweiz/Suisse/Svizzera Zürich Zürich Rodzaj działalności Higher or Secondary Education Establishments Linki Kontakt z organizacją Opens in new window Strona internetowa Opens in new window Uczestnictwo w unijnych programach w zakresie badań i innowacji Opens in new window sieć współpracy HORIZON Opens in new window Koszt całkowity € 2 631 556,00